Machine-learning potentials are accelerating the development of energy materials, especially in identifying phase diagrams and other thermodynamic properties. In this work, we present a neural network potential based on atom-centered symmetry function descriptors to model the energetics of lithium intercalation into graphite. The potential was trained on a dataset of over 9000 diverse lithium–graphite configurations that varied in applied stress and strain, lithium concentration, lithium–carbon and lithium–lithium bond distances, and stacking order to ensure wide sampling of the potential atomic configurations during intercalation. We calculated the energies of these structures using density functional theory (DFT) through the Bayesian error estimation functional with van der Waals correlation exchange-correlation functional, which can accurately describe the van der Waals interactions that are crucial to determining the thermodynamics of this phase space. Bayesian optimization, as implemented in Dragonfly, was used to select optimal set of symmetry function parameters, ultimately resulting in a potential with a prediction error of 8.24 meV atom⁻¹ on unseen test data. The potential can predict energies, structural properties, and elastic constants at an accuracy comparable to other DFT exchange-correlation functionals at a fraction of the computational cost. The accuracy of the potential is also comparable to similar machine-learned potentials describing other systems. We calculate the open circuit voltage with the calculator and find good agreement with experiment, especially in the regime x ≥ 0.3, for x in Li _x C₆. This study further illustrates the power of machine learning potentials, which promises to revolutionize design and optimization of battery materials.

机器学习的潜力正在加速能源材料的发展，特别是在识别相图和其他热力学特性方面。在这项工作中，我们提出了一个基于原子中心对称函数描述符的神经网络电势，以模拟锂嵌入石墨中的能量。对该电位进行了训练，该数据集中包含9000多种不同的锂-石墨构型，这些构型在施加的应力和应变，锂浓度，锂-碳和锂-锂键合距离以及堆叠顺序方面有所不同，以确保在嵌入过程中对潜在的原子构型进行广泛采样。我们通过贝叶斯误差估计函数和范德华关联交换相关函数，使用密度泛函理论（DFT）计算了这些结构的能量，可以准确地描述对于确定该相空间的热力学至关重要的范德华相互作用。贝叶斯优化，如在蜻蜓用于选择最佳的对称函数参数集，最终导致在看不见的测试数据上的预测误差为8.24 meV atom ^-1。电位可以以与计算其他DFT交换相关函数相当的精度预测能量，结构特性和弹性常数。电位的准确性也可与描述其他系统的类似机器学习的电位相媲美。我们计算的开路电压与计算器和找到实验良好的一致性，尤其是在政权X  ≥0.3，用于X李_X Ç ₆ 。这项研究进一步说明了机器学习潜力的力量，这有望彻底改变电池材料的设计和优化。